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Sensitivity analysis for building energy audit calculation methods: Handling the uncertainties in small power load estimation

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  • Rodriguez, Ana
  • Smith, Stefán Thor
  • Potter, Ben

Abstract

The use of static calculation models in building energy audits typically requires estimation of power loads, as appliance load monitoring is both expensive and intrusive. Small power loads are particularly difficult to monitor in detail and so, with increasing proportional contribution to overall building energy consumption, constitute a significant source of uncertainty in estimations. Little research has been conducted on how uncertainty in audit estimations (particularly of small power loads) should be handled and to what extent this impacts on what information is of most value in reducing audit uncertainties. The Morris method for sensitivity analysis has been adapted for small power load estimation to identify which inputs in static calculation models the output estimations are most sensitive to. The method is applied to the TM22 and TM54 audit calculation models from the Chartered Institute of Building Service Engineers (CIBSE); widely used in industry. Results demonstrate how the weight of input uncertainties differs according to the calculation model used. By providing uncertainty estimation in the audit process, auditors can choose an optimal energy assessment strategy for small power load estimations whilst also better understanding the significance of input uncertainty within the different energy calculation models.

Suggested Citation

  • Rodriguez, Ana & Smith, Stefán Thor & Potter, Ben, 2022. "Sensitivity analysis for building energy audit calculation methods: Handling the uncertainties in small power load estimation," Energy, Elsevier, vol. 238(PA).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s036054422101759x
    DOI: 10.1016/j.energy.2021.121511
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    References listed on IDEAS

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    1. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    2. Yan, Chengchu & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "A multi-level energy performance diagnosis method for energy information poor buildings," Energy, Elsevier, vol. 83(C), pages 189-203.
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